Distributed Kalman filtering with event-triggered communication: a robust approach

TL;DR

This paper introduces a robust distributed Kalman filtering method for sensor networks that uses event-triggered communication and accounts for model uncertainty by considering the least favorable model within a Kullback-Leibler ball.

Contribution

It proposes a novel distributed filtering strategy that combines event-triggered communication with robustness against model uncertainties in sensor networks.

Findings

The method effectively handles data transmission limits.

The approach demonstrates robustness to model uncertainties.

Numerical tests show promising performance.

Abstract

We consider the problem of distributed Kalman filtering for sensor networks in the case there is a limit in data transmission and there is model uncertainty. More precisely, we propose a distributed filtering strategy with event-triggered communication in which the state estimators are computed according to the least favorable model. The latter belongs to a ball (in Kullback-Leibler topology) about the nominal model. We also present a preliminary numerical example in order to test the performance of the proposed strategy.